Question: Application Case 8.1 Canadian Football League Optimizes Game Schedule Canadian Football League (CFL) Is Canadas Equivalent Of The U.S. National Football League (NFL). It Had A Challenge Of Organizing 81 Football Games For 9 Teams Over A Period Of 5 Months Optimally While Stabilizing Matching Priorities For Sales Revenue, Television Ratings, And The Team

Application Case 8.1 Canadian Football League Optimizes Game Schedule

Canadian Football League (CFL) is Canadas equivalent of the U.S. National Football League (NFL). It had a challenge of organizing 81 football games for 9 teams over a period of 5 months optimally while stabilizing matching priorities for sales revenue, television ratings, and the team rest days. Other considerations include organizing games over different time zones and the main rivalry games to be held on major public holidays. For any league, a robust schedule is a driving force for a variety of business collaborations, such as coordinating with broadcasting channels and organizing ground ticket sales. If the schedule is not optimized, it would directly hamper the promotions thus resulting in a huge loss of revenue and bad channel ratings. CFL used to create match schedules manually and hence had to figure out finer ways to improve their schedules, taking all the constraints into account. They had tried to work with a consultant to build a comprehensive model for scheduling, but the implementation remained a challenge. The League decided to tackle the issue with the Solver available within Microsoft Excel.

Some of the matching priorities to be balanced while optimizing the schedule were:

1. Sales RevenueSetting a schedule with matches and time slots to those clubs that generate more revenue.
2. Channel RatingsSetting a schedule with games that would improve channel ratings for the broadcasting company.
3. Team Rest DaysSetting a schedule with the two teams playing against each other having enough rest days.

The league decided to improve the match schedules by giving the player rest days as a higher priority, followed by sales revenue and channel scores for the broadcasting company. This is mainly because the sales revenue and channel scores are a byproduct of team players performance on the field, which is directly related to the rest days taken by the teams.

Methodology/Solution

Initially, organizing schedules was a huge task to perform on Excel through the built-in Solver feature. Frontline systems provided a premium version for Solver which allowed the model size to grow from about 200 decisions to 8,000 decisions. The League had to even add in more industry-specific constraints such as telecasting across different time zones, double header games cannot be overlapped, and arch rival games to be scheduled on Labor Day. Added limitations were never simple until the Frontline Systems consultants stepped up to help CFL turn this nonlinear problem into a linear problem. The linear programming engine got the model running. Premium Solver software turned out to be of great help to get an improved schedule.

Results/Benefits

Using the optimized schedule would lead to increased revenue through higher ticket sales and higher TV scores for the broadcasting channels. This was achieved because the tool was able to support added constraints of the vendors with great ease. The optimized schedule pleased most of the leagues stakeholders. This is a repetitive process, but those match schedules were CFLs most advanced season match schedules to date.

What Can We Learn from This Application Case?

By using the Solver add-in for Excel, the CFL made better decisions in scheduling their games by taking stakeholders and industry constraints into consideration, leading to revenue generation and good channel ratings. Thus, an optimized schedule, a purview of prescriptive analytics, derived significant value. According to the case study, the modeler, Mr Trevor Hardy, was an expert Excel user, but not an expert in modeling. However, the ease of use of Excel permitted him to develop a practical application of prescriptive analytics.

5. After reading the Application Case 8.1 related to the Canadian Football League Scheduling, can you (a) number and formulate 3 specific questions (not answers) that the CFL should be asking to increase revenue?

A Shop works a 400 -minute day. The manager of the shop wants an output of 200 units per day for the assembly line that has the elemental tasks shown in the table. Please: 1. Construct the precedence diagram. 2. Assign tasks using the most-following-tasks rule as the primary rule and the greatestpositional-weight rule as the secondary rule. 3. Compute the balance delay and the efficiency of your balance. A Shop works a 400 -minute day. The manager of the shop wants an output of 200 units per day for the assembly line that has the elemental tasks shown in the table. Please: 1. Construct the precedence diagram. 2. Assign tasks using the most-following-tasks rule as the primary rule and the greatestpositional-weight rule as the secondary rule. 3. Compute the balance delay and the efieiency of your balance